Mitigation.
While prior work has explored data augmentation for mitigation [1]}, [2]}, [3]}, [4]}, it has done so with augmentation strategies that are agnostic to the distribution of the training set (e.g., QCEC [1]} simply removes either the tennis rackets or the people, as applicable, uniformly at random for each image).
In contrast, SPIRE aims to use counterfactual images to create a distribution where the label is independent of the spurious object, while minimizing any new correlations between the label and artifacts in the counterfactual images.
For the tennis racket example, this is beneficial for two reasons.
First, making tennis rackets and people independent removes the statistical incentive for the model to rely on this SP.
Second, making them independent means that we increase the number of images of tennis rackets without people which means that images of tennis rackets with and without people have more similar influences on the training loss.
